"""simple docstring"""



import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def 	_lowerCamelCase(	a   ,    a   ,    a				):
       __a       =    ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

       __a       =    (
           ("layer.", "layer_"),
           ("word_embeddings.weight", "word_embeddings"),
           ("position_embeddings.weight", "position_embeddings"),
           ("token_type_embeddings.weight", "token_type_embeddings"),
           (".", "/"),
           ("LayerNorm/weight", "LayerNorm/gamma"),
           ("LayerNorm/bias", "LayerNorm/beta"),
           ("weight", "kernel"),
       )

       if not os.path.isdir(a				):
              os.makedirs(a				)

       __a       =    model.state_dict()

       def to_tf_var_name(a				):
              for patt, repl in iter(a				):
                     __a       =    name.replace(a   ,    a				)
              return F"bert/{name}"

       def create_tf_var(a   ,    a   ,    a				):
              __a       =    tf.dtypes.as_dtype(tensor.dtype				)
              __a       =    tf.get_variable(dtype=a   ,    shape=tensor.shape   ,    name=a   ,    initializer=tf.zeros_initializer()				)
              session.run(tf.variables_initializer([tf_var]				)				)
              session.run(a				)
              return tf_var

       tf.reset_default_graph()
       with tf.Session() as session:
              for var_name in state_dict:
                     __a       =    to_tf_var_name(a				)
                     __a       =    state_dict[var_name].numpy()
                     if any(x in var_name for x in tensors_to_transpose				):
                            __a       =    torch_tensor.T
                     __a       =    create_tf_var(tensor=a   ,    name=a   ,    session=a				)
                     tf.keras.backend.set_value(a   ,    a				)
                     __a       =    session.run(a				)
                     print(F"Successfully created {tf_name}: {np.allclose(a   ,    a				)}"				)

              __a       =    tf.train.Saver(tf.trainable_variables()				)
              saver.save(a   ,    os.path.join(a   ,    model_name.replace("-"   ,    "_"				) + ".ckpt"				)				)



def 	_lowerCamelCase(	a=None				):
       __a       =    argparse.ArgumentParser()
       parser.add_argument("--model_name"   ,    type=a   ,    required=a   ,    help="model name e.g. bert-base-uncased"				)
       parser.add_argument(
           "--cache_dir"   ,    type=a   ,    default=a   ,    required=a   ,    help="Directory containing pytorch model"				)
       parser.add_argument("--pytorch_model_path"   ,    type=a   ,    required=a   ,    help="/path/to/<pytorch-model-name>.bin"				)
       parser.add_argument("--tf_cache_dir"   ,    type=a   ,    required=a   ,    help="Directory in which to save tensorflow model"				)
       __a       =    parser.parse_args(a				)

       __a       =    BertModel.from_pretrained(
           pretrained_model_name_or_path=args.model_name   ,    state_dict=torch.load(args.pytorch_model_path				)   ,    cache_dir=args.cache_dir   ,    )

       convert_pytorch_checkpoint_to_tf(model=a   ,    ckpt_dir=args.tf_cache_dir   ,    model_name=args.model_name				)


if __name__ == "__main__":
   main()

"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
